With the deep integration of the Internet of Vehicles (IoV) and mobile edge computing (MEC), achieving efficient task offloading while ensuring low latency and high reliability has become a key challenge. Although the Harris hawks optimization (HHO) algorithm demonstrates strong global search capability, it still suffers from limitations in population initialization, exploration–exploitation transition, and diversity maintenance, making it prone to premature convergence. To address these issues, this study proposes a dynamic dual-population Harris hawks optimization (DDHHO) algorithm. The proposed algorithm introduces a dynamic dual-population co-evolution (DDPC) mechanism, combined with L-T chaotic initialization, nonlinear escape energy, and a nonlinear jump strategy, to adaptively balance global exploration and local exploitation. Experimental results show that in the IoV-MEC task offloading scenario, DDHHO reduces the total system cost by approximately 2.5%, 3.2%, 4.9%, 6.0%, and 7.9% compared with the mixed-strategy HHO (MSHHO), original HHO, MASSFOA, IPSO and PSO, respectively. Moreover, DDHHO exhibits faster convergence speed and higher stability in joint latency-energy optimization. These results verify the effectiveness and superiority of DDHHO, providing an efficient, stable, and scalable optimization solution for resource management in IoV-MEC systems.